4 Primary analysis
4.1 Model
RICLPM_PRIM <- '
SLEEPx =~ 1*SLEEP_w1 + 1*SLEEP_w2 + 1*SLEEP_w3
ADHDy =~ 1*ADHD_w1 + 1*ADHD_w2 + 1*ADHD_w3
wSLEEP_w1 =~ 1*SLEEP_w1
wSLEEP_w2 =~ 1*SLEEP_w2
wSLEEP_w3 =~ 1*SLEEP_w3
wADHD_w1 =~ 1*ADHD_w1
wADHD_w2 =~ 1*ADHD_w2
wADHD_w3 =~ 1*ADHD_w3
wSLEEP_w2 + wADHD_w2 ~ wSLEEP_w1 + wADHD_w1
wSLEEP_w3 + wADHD_w3 ~ wSLEEP_w2 + wADHD_w2
wSLEEP_w1 ~~ wADHD_w1
wSLEEP_w2 ~~ wADHD_w2
wSLEEP_w3 ~~ wADHD_w3
SLEEPx ~~ SLEEPx
ADHDy ~~ ADHDy
SLEEPx ~~ ADHDy
wSLEEP_w1 ~~ wSLEEP_w1
wADHD_w1 ~~ wADHD_w1
wSLEEP_w2 ~~ wSLEEP_w2
wADHD_w2 ~~ wADHD_w2
wSLEEP_w3 ~~ wSLEEP_w3
wADHD_w3 ~~ wADHD_w3
'
4.2 Complete case analysis
4.2.1 RI-CLPM output
RICLPM_CCA <- lavaan::lavaan(RICLPM_PRIM,
data = df,
estimator = "WLSMV",
meanstructure = TRUE,
int.ov.free = TRUE)
lavaan::summary(RICLPM_CCA, standardized = TRUE, fit.measures = TRUE, ci = TRUE)
## lavaan 0.6-9 ended normally after 172 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 26
##
## Used Total
## Number of observations 256 1055
##
## Model Test User Model:
## Standard Robust
## Test Statistic 0.349 0.820
## Degrees of freedom 1 1
## P-value (Chi-square) 0.555 0.365
## Scaling correction factor 0.425
## Shift parameter 0.000
## simple second-order correction
##
## Model Test Baseline Model:
##
## Test statistic 122.611 82.867
## Degrees of freedom 15 15
## P-value 0.000 0.000
## Scaling correction factor 1.586
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000 1.000
## Tucker-Lewis Index (TLI) 1.091 1.040
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000 0.000
## 90 Percent confidence interval - lower 0.000 0.000
## 90 Percent confidence interval - upper 0.138 0.160
## P-value RMSEA <= 0.05 0.665 0.502
##
## Robust RMSEA NA
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.007 0.007
##
## Parameter Estimates:
##
## Standard errors Robust.sem
## Information Expected
## Information saturated (h1) model Unstructured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## SLEEPx =~
## SLEEP_w1 1.000 1.000 1.000
## SLEEP_w2 1.000 1.000 1.000
## SLEEP_w3 1.000 1.000 1.000
## ADHDy =~
## ADHD_w1 1.000 1.000 1.000
## ADHD_w2 1.000 1.000 1.000
## ADHD_w3 1.000 1.000 1.000
## wSLEEP_w1 =~
## SLEEP_w1 1.000 1.000 1.000
## wSLEEP_w2 =~
## SLEEP_w2 1.000 1.000 1.000
## wSLEEP_w3 =~
## SLEEP_w3 1.000 1.000 1.000
## wADHD_w1 =~
## ADHD_w1 1.000 1.000 1.000
## wADHD_w2 =~
## ADHD_w2 1.000 1.000 1.000
## wADHD_w3 =~
## ADHD_w3 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.455 0.504
## 0.455 0.444
## 0.455 0.387
##
## 2.551 0.496
## 2.551 0.455
## 2.551 0.548
##
## 0.780 0.864
##
## 0.918 0.896
##
## 1.085 0.922
##
## 4.464 0.868
##
## 4.998 0.891
##
## 3.896 0.837
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wSLEEP_w2 ~
## wSLEEP_w1 0.279 0.150 1.851 0.064 -0.016 0.574
## wADHD_w1 -0.008 0.017 -0.491 0.624 -0.041 0.025
## wADHD_w2 ~
## wSLEEP_w1 0.278 0.585 0.475 0.635 -0.869 1.425
## wADHD_w1 0.508 0.106 4.782 0.000 0.300 0.716
## wSLEEP_w3 ~
## wSLEEP_w2 0.208 0.120 1.732 0.083 -0.027 0.443
## wADHD_w2 0.004 0.021 0.182 0.856 -0.037 0.044
## wADHD_w3 ~
## wSLEEP_w2 0.654 0.327 2.002 0.045 0.014 1.294
## wADHD_w2 0.216 0.110 1.965 0.049 0.001 0.431
## Std.lv Std.all
##
## 0.237 0.237
## -0.040 -0.040
##
## 0.043 0.043
## 0.454 0.454
##
## 0.176 0.176
## 0.017 0.017
##
## 0.154 0.154
## 0.277 0.277
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wSLEEP_w1 ~~
## wADHD_w1 0.857 0.565 1.518 0.129 -0.250 1.964
## .wSLEEP_w2 ~~
## .wADHD_w2 0.960 0.303 3.167 0.002 0.366 1.554
## .wSLEEP_w3 ~~
## .wADHD_w3 1.400 0.575 2.436 0.015 0.274 2.526
## SLEEPx ~~
## ADHDy 0.093 0.264 0.352 0.725 -0.425 0.611
## Std.lv Std.all
##
## 0.246 0.246
##
## 0.243 0.243
##
## 0.359 0.359
##
## 0.080 0.080
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .SLEEP_w1 0.821 0.056 14.546 0.000 0.710 0.931
## .SLEEP_w2 0.915 0.064 14.262 0.000 0.789 1.041
## .SLEEP_w3 1.200 0.074 16.315 0.000 1.056 1.344
## .ADHD_w1 1.907 0.321 5.934 0.000 1.277 2.537
## .ADHD_w2 3.069 0.351 8.736 0.000 2.380 3.758
## .ADHD_w3 2.539 0.291 8.724 0.000 1.969 3.110
## SLEEPx 0.000 0.000 0.000
## ADHDy 0.000 0.000 0.000
## wSLEEP_w1 0.000 0.000 0.000
## .wSLEEP_w2 0.000 0.000 0.000
## .wSLEEP_w3 0.000 0.000 0.000
## wADHD_w1 0.000 0.000 0.000
## .wADHD_w2 0.000 0.000 0.000
## .wADHD_w3 0.000 0.000 0.000
## Std.lv Std.all
## 0.821 0.909
## 0.915 0.893
## 1.200 1.020
## 1.907 0.371
## 3.069 0.547
## 2.539 0.545
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## SLEEPx 0.207 0.089 2.332 0.020 0.033 0.381
## ADHDy 6.509 3.575 1.821 0.069 -0.498 13.515
## wSLEEP_w1 0.608 0.152 3.997 0.000 0.310 0.906
## wADHD_w1 19.927 4.967 4.012 0.000 10.192 29.662
## .wSLEEP_w2 0.798 0.118 6.751 0.000 0.566 1.030
## .wADHD_w2 19.548 3.842 5.088 0.000 12.017 27.079
## .wSLEEP_w3 1.140 0.150 7.614 0.000 0.846 1.433
## .wADHD_w3 13.360 4.859 2.750 0.006 3.837 22.882
## .SLEEP_w1 0.000 0.000 0.000
## .SLEEP_w2 0.000 0.000 0.000
## .SLEEP_w3 0.000 0.000 0.000
## .ADHD_w1 0.000 0.000 0.000
## .ADHD_w2 0.000 0.000 0.000
## .ADHD_w3 0.000 0.000 0.000
## Std.lv Std.all
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.947 0.947
## 0.782 0.782
## 0.967 0.967
## 0.880 0.880
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
4.2.2 % of variance explained
lavInspect(RICLPM_CCA, "r2")
## wSLEEP_w2 wADHD_w2 wSLEEP_w3 wADHD_w3 SLEEP_w1 SLEEP_w2 SLEEP_w3 ADHD_w1
## 0.053 0.218 0.033 0.120 1.000 1.000 1.000 1.000
## ADHD_w2 ADHD_w3
## 1.000 1.000